GWO-Based Joint Optimization of Millimeter-Wave System and Multilayer Perceptron for Archaeological Application.

Recently, low THz radar-based measurement and classification for archaeology emerged as a new imaging modality. In this paper, we investigate the classification of pottery shards, a key enabler to understand how the agriculture was introduced from the Fertile Crescent to Europe. Our purpose is to jointly design the measuring radar system and the classification neural network, seeking the maximal compactness and the minimal cost, both directly related to the number of sensors. We aim to select the least possible number of sensors and place them adequately, while minimizing the false recognition rate. For this, we propose a novel version of the Binary Grey Wolf Optimizer, designed to reduce the number of sensors, and a Ternary Grey Wolf Optimizer. Together with the Continuous Grey Wolf Optimizer, they yield the CBTGWO (Continuous Binary Ternary Grey Wolf Optimizer). Working with 7 frequencies and starting with 37 sensors, the CBTGWO selects a single sensor and yields a 0-valued false recognition rate. In a single-frequency scenario, starting with 217 sensors, the CBTGWO selects 2 sensors. The false recognition rate is 2%. The acquisition time is 3.2 s, outperforming the GWO and adaptive mixed GWO, which yield 86.4 and 396.6 s.

Anomaly classification by inserting prior knowledge into a max-tree based method for divertor hot spot characterization on WEST tokamak.

The divertor of WEST (W Environment in Steady-state Tokamak) is the main component for plasma control and exhaust. It receives high heat fluxes, which can cause damage to plasma facing units above the allowable heat flux. Improving the operation safety on the actively cooled tungsten divertor is being researched in place at WEST, toward providing divertor monitoring solution for ITER. Divertor operation safety relies on detecting, monitoring, and classifying all hot spots on the divertor surface using infrared (IR) cameras. In this paper, a method based on max-tree representation and attributes of IR images is used to classify normal from abnormal strikelines on the divertor. The proposed method requires only high-level prior knowledge of abnormal temperatures and divertor structure but does not require any labeled data, unlike existing methods, such as support vector machines (SVMs) or convolutional neural networks (CNNs). The max-tree classifier method is tested on real IR images from the WEST tokamak and shows that 88% of hot spots are accurately classified with a small enough calculation duration that can be performed between two pulses.

Subspace-based and DIRECT algorithms for distorted circular contour estimation.

Circular features are commonly sought in digital image processing. The subspace-based line detection (SLIDE) method proposed to estimate the center and the radius of a single circle. In this paper, we introduce a novel method for estimating several radii while extending the circle estimation to retrieve circular-like distorted contours. Particularly, we develop and validate a new model for virtual signal generation by simulating a circular antenna. The circle center is estimated by the SLIDE method. A variable speed propagation scheme toward the circular antenna yields a linear phase signal. Therefore, a high-resolution method provides the radius. Either the gradient method or the more robust combination of dividing rectangles and spline interpolation can extend this method extend this method for free form object segmentation. The retrieval of multiple non concentric circles and rotated ellipses is also considered. To evaluate the performance of the proposed methods, we compare them with a least-squares method, Hough transform, and gradient vector flow. We apply the proposed method to hand-made images while considering some real-world images.